A Quick Introduction to Version Control with Git and GitHub

Blischak, John; Davenport, Emily; Wilson, Gregory V.

doi:10.1371/journal.pcbi.1004668

Cited by 152 publications

(135 citation statements)

References 6 publications

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“…This saves time that would otherwise be spent duplicating, renaming and organizing files to preserve past versions. It also makes folders easier to navigate since they are no longer overcrowded with multiple files suffixed with dates or ini tials (for example, 'final_JL 2012 02 26.csv') [67][68][69] . Once Git is con figured on each team member's machine, they work as before but frequently commit to saving a snapshot of their files, along with a human readable 'commit message' 67,68 .…”

Section: Perspective Nature Ecology and Evolutionmentioning

confidence: 99%

“…GitHub is one of many web based platforms that enables files tracked with Git to be col laboratively shared online so contributors can keep their work syn chronized 45,68,69 , and it is increasingly being adopted by scientific communities for project management 71 . Versioned files are synced online with GitHub similar to the way Dropbox operates, except syncs require a committed, human readable message and reflect deliberate snapshots of changes made that are attributed to the user, line by line, through time.…”

Section: Collaboration Within Our Teammentioning

confidence: 99%

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Our path to better science in less time using open data science tools

Best²,

et al. 2017

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Section: Perspective Nature Ecology and Evolutionmentioning

confidence: 99%

Section: Collaboration Within Our Teammentioning

confidence: 99%

Our path to better science in less time using open data science tools

Best²,

et al. 2017

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“…For instance, a key tool that all research programmers should incorporate into their workflow is the use of a Version Control System (VCS) such as git [28] or subversion (SVN). A VCS provides a way for taking snapshots of evolving code that allow tracking of changes, and for reverting these if necessary (e.g., after making a change that ends up breaking the functionality of the code).…”

Section: Open Source: Sustainable Software For Sustainable Sciencementioning

confidence: 99%

Do you speak open science? Resources and tips to learn the language

Masuzzo

Martens

2017

Preprint

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The internet era, large-scale computing and storage resources, mobile devices, social media, and their high uptake among different groups of people, have all deeply changed the way knowledge is created, communicated, and further deployed. These advances have enabled a radical transformation of the practice of science, which is now more open, more global and collaborative, and closer to society than ever. Open science has therefore become an increasingly important topic.Moreover, as open science is actively pursued by several high-profile funders and institutions, it has fast become a crucial matter to all researchers. However, because this widespread interest in open science has emerged relatively recently, its definition and implementation are constantly shifting and evolving, sometimes leaving researchers in doubt about how to adopt open science, and which are the best practices to follow.

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“…Since its initial launch in 2008, GitHub has grown in popularity within the bioinformatics field, as demonstrated by the proportion of articles in the journal Bioinformatics mentioning GitHub in the abstract (Fig 1). For an excellent explanation of Git and GitHub including additional definitions, see [14]. …”

Section: Introductionmentioning

confidence: 99%

A large-scale analysis of bioinformatics code on GitHub

et al. 2018

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In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/github-bioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.

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A Quick Introduction to Version Control with Git and GitHub

Cited by 152 publications

References 6 publications

Our path to better science in less time using open data science tools

Our path to better science in less time using open data science tools

Do you speak open science? Resources and tips to learn the language

A large-scale analysis of bioinformatics code on GitHub

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